Fuzzylogic,duetoitsnatureofcharacterizingtheambiguouscaseswithmultiplevalues,offersrich resourcesfordealingwithassessmentsituations,afactthatgaveusseveraltimesinpasttheimpulse toapplyitsprinciplesforsuchkindofsituations.FuzzyNumbers(FNs)playafundamentalrolein fuzzymathematics,analogoustotheroleplayedbytheordinarynumbersinclassicalma...

In many modelling problems, there is some inherent monotone relationship between one or more of the input variables and the output variable. We consider the prototypical case of an increasing relationship between each of the input variables and the output variable. When using fuzzy rule-based models, this desired monotonicity is reflected in the rule base, given an appropriate ordering on the f...

This paper builds on a previously proposed approach where fuzzy logic is used to incorporate linguistic variables in system dynamics modeling. The motivation for this approach is to include vague yet dynamic variables that are combined in a meaningful way. The essence of our approach requires the definition of membership functions as representations of the degree to which specific variable attr...

Article history: Received 10 August 2012 Received in revised form 29 January 2013 Accepted 29 April 2013 Available online 14 May 2013

A fuzzy inventory model with price dependent demand is considered. The shortages are allowed . The objective is to determine the optimal total cost and the optimal time length of the plan for the proposed model. The trapezoidal fuzzy numbers are introduced to achieve this objective. The signed distance method is used for defuzzification process. To illustrate the results of this model, sensitiv...

The Center of Gravity (COG) method is one of the most popular defuzzification techniques of fuzzy mathematics. In earlier works the COG technique was properly adapted to be used as an assessment model (RFAM) and several variations of it (GRFAM, TFAM and TpFAM) were also constructed for the same purpose. In this paper the outcomes of all these models are compared to the corresponding outcomes of...

Article history: Received 17 August 2012 Received in revised form 24 April 2013 Accepted 24 April 2013 Available online 7 May 2013

We present some interesting properties related to the area compensation procedure to compare fuzzy numbers. It has been proved that this method produces more than a fuzzy interval order: it induces a ranking of fuzzy numbers. Some further results are given about the transitivity property and about computational aspects. Extensions to non-normal fuzzy numbers and fuzzy quantities are also proposed.